Abstract

Spiking neural networks (SNN) are powerful computational model inspired by the human neural system for engineers and neuroscientists to simulate intelligent computation of the brain. Inspired by the visual system, various spiking neural network models have been used to process visual images. However, it is time-consuming to simulate a large scale of spiking neurons in the networks using CPU programming. Spiking neural networks inherit intrinsically parallel mechanism from biological system. A massively parallel implementation technology is required to simulate them. To address this issue, modern Graphic Processing Units (GPUs), which have parallel array of streaming multiprocessors, allow many thousands of lightweight threads to be run, is proposed and proved as a pertinent solution. This paper presents an approach for implementation of an SNN model which performs color image segmentation on GPU. This approach is then compared with an equivalent implementation on an Intel Xeon CPU. The results show that the GPU approach was found to provide a 31 times faster than the CPU implementation.

abstract = "Spiking neural networks (SNN) are powerful computational model inspired by the human neural system for engineers and neuroscientists to simulate intelligent computation of the brain. Inspired by the visual system, various spiking neural network models have been used to process visual images. However, it is time-consuming to simulate a large scale of spiking neurons in the networks using CPU programming. Spiking neural networks inherit intrinsically parallel mechanism from biological system. A massively parallel implementation technology is required to simulate them. To address this issue, modern Graphic Processing Units (GPUs), which have parallel array of streaming multiprocessors, allow many thousands of lightweight threads to be run, is proposed and proved as a pertinent solution. This paper presents an approach for implementation of an SNN model which performs color image segmentation on GPU. This approach is then compared with an equivalent implementation on an Intel Xeon CPU. The results show that the GPU approach was found to provide a 31 times faster than the CPU implementation.",

N2 - Spiking neural networks (SNN) are powerful computational model inspired by the human neural system for engineers and neuroscientists to simulate intelligent computation of the brain. Inspired by the visual system, various spiking neural network models have been used to process visual images. However, it is time-consuming to simulate a large scale of spiking neurons in the networks using CPU programming. Spiking neural networks inherit intrinsically parallel mechanism from biological system. A massively parallel implementation technology is required to simulate them. To address this issue, modern Graphic Processing Units (GPUs), which have parallel array of streaming multiprocessors, allow many thousands of lightweight threads to be run, is proposed and proved as a pertinent solution. This paper presents an approach for implementation of an SNN model which performs color image segmentation on GPU. This approach is then compared with an equivalent implementation on an Intel Xeon CPU. The results show that the GPU approach was found to provide a 31 times faster than the CPU implementation.

AB - Spiking neural networks (SNN) are powerful computational model inspired by the human neural system for engineers and neuroscientists to simulate intelligent computation of the brain. Inspired by the visual system, various spiking neural network models have been used to process visual images. However, it is time-consuming to simulate a large scale of spiking neurons in the networks using CPU programming. Spiking neural networks inherit intrinsically parallel mechanism from biological system. A massively parallel implementation technology is required to simulate them. To address this issue, modern Graphic Processing Units (GPUs), which have parallel array of streaming multiprocessors, allow many thousands of lightweight threads to be run, is proposed and proved as a pertinent solution. This paper presents an approach for implementation of an SNN model which performs color image segmentation on GPU. This approach is then compared with an equivalent implementation on an Intel Xeon CPU. The results show that the GPU approach was found to provide a 31 times faster than the CPU implementation.